A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

Game Theory in Communications:a Study of Two Scenarios

Multi-user communication theory typically studies the fundamental limits of communication systems, and considers communication schemes that approach or even achieve these limits. The functioning of many such schemes assumes that users always cooperate, even when it is not in their own best interest. In practice, this assumption need not be fulfilled, as rational communication participants are often only interested in maximizing their own communication experience, and may behave in an undesirable manner from the system's point of view. Thus, communication systems may operate differently than intended if the behavior of individual participants is not taken into account. In this thesis, we study how users make decisions in wireless settings, by considering their preferences and how they interact with each other. We investigate whether the outcomes of their decisions are desirable, and, if not, what can be done to improve them. In particular, we focus on two related issues. The first is the decision-making of communication users in the absence of any central authority, which we consider in the context of the Gaussian multiple access channel. The second is the pricing of wireless resources, which we consider in the context of the competition of wireless service providers for users who are not contractually tied to any provider, but free to choose the one offering the best tradeoff of parameters. In the first part of the thesis, we model the interaction of self-interested users in a Gaussian multiple access channel using non-cooperative game theory. We demonstrate that the lack of infrastructure leads to an inefficient outcome for users who interact only once, specifically due to the lack of coordination between users. Using evolutionary game theory, we show that this inefficient outcome would also arise as a result of repeated interaction of many individuals over time. On the other hand, if the users correlate their decoding schedule with the outcome of some publicly observed (pseudo) random variable, the resulting outcome is efficient. This shows that sometimes it takes very little intervention on the part of the system planner to make sure that users choose a desirable operating point. In the second part of the thesis, we consider the competition of wireless service providers for users who are free to choose their service provider based on their channel parameters and the resource price. We model this situation as a two-stage game where the providers announce unit resource prices in the first stage and the users choose how much resource they want to purchase from each provider in the second stage. Under fairly general conditions, we show that the competitive interaction of users and providers results in socially optimal resource allocation. We also provide a decentralized primal-dual algorithm and prove its convergence to the socially optimal outcome

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

Game theory for cooperation in multi-access edge computing

Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.info:eu-repo/semantics/acceptedVersio

Distributed optimisation techniques for wireless networks

Alongside the ever increasing traffic demand, the fifth generation (5G) cellular network architecture is being proposed to provide better quality of service, increased data rate, decreased latency, and increased capacity. Without any doubt, the 5G cellular network will comprise of ultra-dense networks and multiple input multiple output technologies. This will make the current centralised solutions impractical due to increased complexity. Moreover, the amount of coordination information that needs to be transported over the backhaul links will be increased. Distributed or decentralised solutions are promising to provide better alternatives. This thesis proposes new distributed algorithms for wireless networks which aim to reduce the amount of system overheads in the backhaul links and the system complexity. The analysis of conflicts amongst transmitters, and resource allocation are conducted via the use of game theory, convex optimisation, and auction theory. Firstly, game-theoretic model is used to analyse a mixed quality of service (QoS) strategic non-cooperative game (SNG), for a two-user multiple-input single-output (MISO) interference channel. The players are considered to have different objectives. Following this, the mixed QoS SNG is extended to a multicell multiuser network in terms of signal-to-interference-and-noise ratio (SINR) requirement. In the multicell multiuser setting, each transmitter is assumed to be serving real time users (RTUs) and non-real time users (NRTUs), simultaneously. A novel mixed QoS SNG algorithm is proposed, with its operating point identified as the Nash equilibrium-mixed QoS (NE-mixed QoS). Nash, Kalai-Smorodinsky, and Egalitarian bargain solutions are then proposed to improve the performance of the NE-mixed QoS. The performance of the bargain solutions are observed to be comparable to the centralised solutions. Secondly, user offloading and user association problems are addressed for small cells using auction theory. The main base station wishes to offload some of its users to privately owned small cell access points. A novel bid-wait-auction (BWA) algorithm, which allows single-item bidding at each auction round, is designed to decompose the combinatorial mathematical nature of the problem. An analysis on the existence and uniqueness of the dominant strategy equilibrium is conducted. The BWA is then used to form the forward BWA (FBWA) and the backward BWA (BBWA). It is observed that the BBWA allows more users to be admitted as compared to the FBWA. Finally, simultaneous multiple-round ascending auction (SMRA), altered SMRA (ASMRA), sequential combinatorial auction with item bidding (SCAIB), and repetitive combinatorial auction with item bidding (RCAIB) algorithms are proposed to perform user offloading and user association for small cells. These algorithms are able to allow bundle bidding. It is then proven that, truthful bidding is individually rational and leads to Walrasian equilibrium. The performance of the proposed auction based algorithms is evaluated. It is observed that the proposed algorithms match the performance of the centralised solutions when the guest users have low target rates. The SCAIB algorithm is shown to be the most preferred as it provides high admission rate and competitive revenue to the bidders

Design of Wireless Communication Networks for Cyber-Physical Systems with Application to Smart Grid

Cyber-Physical Systems (CPS) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. On one hand, CPS are generally large with components spatially distributed in physical world that has lots of dynamics; on the other hand, CPS are connected, and must be robust and responsive. Smart electric grid, smart transportation system are examples of emerging CPS that have significant and far-reaching impact on our daily life. In this dissertation, we design wireless communication system for CPS. To make CPS robust and responsive, it is critical to have a communication subsystem that is reliable, adaptive, and scalable. Our design uses a layered structure, which includes physical layer, multiple access layer, network layer, and application layer. Emphases are placed on multiple access and network layer. At multiple access layer, we have designed three approaches, namely compressed multiple access, sample-contention multiple access, and prioritized multiple access, for reliable and selective multiple access. At network layer, we focus on the problem of creating reliable route, with service interruption anticipated. We propose two methods: the first method is a centralized one that creates backup path around zones posing high interruption risk; the other method is a distributed one that utilizes Ant Colony Optimization (ACO) and positive feedback, and is able to update multipath dynamically. Applications are treated as subscribers to the data service provided by the communication system. Their data quality requirements and Quality of Service (QoS) feedback are incorporated into cross-layer optimization in our design. We have evaluated our design through both simulation and testbed. Our design demonstrates desired reliability, scalability and timeliness in data transmission. Performance gain is observed over conventional approaches as such random access

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

D13.2 Techniques and performance analysis on energy- and bandwidth-efficient communications and networking

Deliverable D13.2 del projecte europeu NEWCOM#The report presents the status of the research work of the various Joint Research Activities (JRA) in WP1.3 and the results that were developed up to the second year of the project. For each activity there is a description, an illustration of the adherence to and relevance with the identified fundamental open issues, a short presentation of the main results, and a roadmap for the future joint research. In the Annex, for each JRA, the main technical details on specific scientific activities are described in detail.Peer ReviewedPostprint (published version